CN105634058B - A kind of intelligent equalization method of battery pack and intelligent equalization system - Google Patents
A kind of intelligent equalization method of battery pack and intelligent equalization system Download PDFInfo
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- CN105634058B CN105634058B CN201610046201.4A CN201610046201A CN105634058B CN 105634058 B CN105634058 B CN 105634058B CN 201610046201 A CN201610046201 A CN 201610046201A CN 105634058 B CN105634058 B CN 105634058B
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0013—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries acting upon several batteries simultaneously or sequentially
- H02J7/0014—Circuits for equalisation of charge between batteries
Abstract
It is described to establish unit including establishing unit, training unit and balanced unit the present invention relates to a kind of intelligent equalization method of battery pack and intelligent equalization system, for establishing neural network model；For establishing training sample group, for each battery pack in the training sample group, neutral net is trained by genetic algorithm for the training unit, can meet the equalization performance requirement of default battery pack until the neural network model converges to output signal；The balanced unit, for according to the output signal, being carried out to the battery pack balanced.As it can be seen that the intelligent equalization method of the battery pack and intelligent equalization system, can effective intelligent equalization based on genetic algorithm neural fusion battery pack, exploitativeness is strong.
Description
Technical field
The present invention relates to lithium battery management system technical field more particularly to the intelligent equalization methods and intelligence of a kind of battery pack
It can equal balance system.
Background technology
Lithium battery group is primary to be applied to New-energy electric vehicle industry and energy-storage system product.New energy electric vapour at present
Vehicle and battery energy storage industry development are swift and violent.For lithium battery for electric vehicle group and large and medium-sized battery energy storage system, design is good
BMS (Battery Management System, battery management system) seem particularly important.Wherein BMS needs are realized most
One of important function is exactly the equalization function of battery pack.The manufacture of lithium battery group, in any case all always can be electric there are monomer
Difference between pond, which, which is mainly manifested in internal resistance, to change with time passage and temperature fluctuation, so that battery
Capacity can difference.The single battery of high internal resistance and low capacity is present with the voltage of bigger when discharge current is big
The amplitude of oscillation.So the single battery big with normal cell difference is easier to damage, become the short slab of entire battery pack, form wood
Bucket effect causes the degradation of whole group battery.Current main BMS equalization methods have following two：
1) active balanced way：Using the accessory power supply single battery electric energy supplement low to energy, make it to energy height
Single battery dress, make up short slab.The mode of energy transfer can also be used, such as using capacitance as terminal progress energy
Transfer, by the single battery of the energy transfer of the single battery of high-energy to low energy.
2) passive type balanced way：The high single battery of energy is discharged, it is made to be dressed to the low single battery of energy.
In fact, no matter using which kind of above balanced way, to improve the validity of battery pack balancing, core technology it
One is that and holds balanced opportunity, such as when carries out equilibrium, balanced how many energy, is balanced to which kind of degree is just calculated properly etc..
In this regard, solution of the current industry still without reliable maturation, typically rule of thumb, when the voltage difference between single battery reaches
Just start equilibrium during to certain value, equilibrium is closed when voltage reaches unanimity between single battery or rule of thumb using one
Fixed fixation algorithm determines balanced opportunity.But due to the discharge performance of lithium battery group can be with discharge time, discharge-rate
It is different and continually changing, so these ways, which do not ensure that, obtains good portfolio effect.
The content of the invention
It is an object of the invention to propose a kind of intelligent equalization method of battery pack and intelligent equalization system, can be based on losing
Effective intelligent equalization of propagation algorithm neural fusion battery pack, exploitativeness are strong.
For this purpose, the present invention uses following technical scheme：
In a first aspect, a kind of intelligent equalization method of battery pack is provided, including：
Establish neural network model, the input information of the neural network model include the accumulated cycles of battery pack,
In accumulated discharge duration, the preceding once capacity of equilibrium, current discharge capacity, the remaining capacity of prediction, discharge-rate at least
One；
Training sample group is established, for each battery pack in the training sample group, nerve is trained by genetic algorithm
Network can meet the equalization performance requirement of default battery pack until the neural network model converges to output signal；Wherein,
The output signal includes the capacity of the needs equilibrium of the battery pack, opens the balanced time, opens in balanced electric current
At least one of；
According to the output signal, the battery pack is carried out balanced.
Wherein, the input information of the first time training of the neural network model is the capacity of default equilibrium, described pre-
If balanced capacity be voltage difference between every sub- battery according to battery pack and obtain.
Wherein, it is described that neutral net is trained by genetic algorithm, including the following operation performed successively：
(1) initial weights, threshold value are generated for neutral net by indirect assignment and encoded；
(2) using the weights of neutral net as digital chromosome, determine the nerve network input parameter, pass through nerve
Network carries out computing to input parameter, generates the network output of respective digital chromosome；
(3) fitness of chromosome is calculated according to fitness algorithm；
(4) by selection algorithm, the higher chromosome of fitness is selected；
(5) by mutation algorithm and Hybrid Algorithm, selected chromosome is hybridized, make a variation generation a new generation kind
Group；
(6) operation (2) is returned, can meet default battery pack until the neural network model converges to output signal
Equalization performance requirement.
Wherein, it is described to need balanced capacity, including：
It is described to need balanced capacity to be transferred into or being shifted by balanced sub- battery needs for active balanced way
The energy gone out；
For passive type balanced way, the balanced capacity of the needs is the energy consumed by balanced sub- battery needs
Amount.
Wherein, the battery pack is lithium battery group.
Second aspect provides a kind of intelligent equalization system of battery pack, including：
Unit is established, for establishing neural network model, the input information of the neural network model includes battery pack
Accumulated cycles, accumulated discharge duration, the preceding once capacity of equilibrium, current discharge capacity, the remaining capacity of prediction, electric discharge
At least one of in multiplying power；
Training unit for establishing training sample group, for each battery pack in the training sample group, passes through heredity
Algorithm for Training neutral net can meet the harmony of default battery pack until the neural network model converges to output signal
It can requirement；Wherein, the output signal includes needing balanced capacity, opening the balanced time, open for the battery pack
At least one of in the electric current of weighing apparatus；
Balanced unit, for according to the output signal, being carried out to the battery pack balanced.
Wherein, the input information of the first time training of the neural network model is the capacity of default equilibrium, described pre-
If balanced capacity be voltage difference between every sub- battery according to battery pack and obtain.
Wherein, it is described that neutral net is trained by genetic algorithm, including the following operation performed successively：
(1) initial weights, threshold value are generated for neutral net by indirect assignment and encoded；
(2) using the weights of neutral net as digital chromosome, determine the nerve network input parameter, pass through nerve
Network carries out computing to input parameter, generates the network output of respective digital chromosome；
(3) fitness of chromosome is calculated according to fitness algorithm；
(4) by selection algorithm, the higher chromosome of fitness is selected；
(5) by mutation algorithm and Hybrid Algorithm, selected chromosome is hybridized, make a variation generation a new generation kind
Group；
(6) operation (2) is returned, can meet default battery pack until the neural network model converges to output signal
Equalization performance requirement.
Wherein, it is described to need balanced capacity, including：
It is described to need balanced capacity to be transferred into or being shifted by balanced sub- battery needs for active balanced way
The energy gone out；
For passive type balanced way, the balanced capacity of the needs is the energy consumed by balanced sub- battery needs
Amount.
Wherein, the battery pack is lithium battery group.
The beneficial effects of the present invention are：A kind of intelligent equalization method of battery pack and intelligent equalization system, including establishing
Unit, training unit and balanced unit, it is described to establish unit, for establishing neural network model, the neural network model
Accumulated cycles of the input information including battery pack, accumulated discharge duration, the preceding once capacity of equilibrium, current appearance of having discharged
At least one of in amount, the remaining capacity of prediction, discharge-rate；The training unit, for establishing training sample group, for institute
Each battery pack in training sample group is stated, neutral net is trained by genetic algorithm, until the neural network model is restrained
The equalization performance requirement of default battery pack can be met to output signal；Wherein, the output signal includes the battery pack
At least one of in the electric current for needing balanced capacity, the time for opening equilibrium, unlatching balanced；The balanced unit, for root
According to the output signal, the battery pack is carried out balanced.As it can be seen that the intelligent equalization method of the battery pack and intelligent equalization system
System, can effective intelligent equalization based on genetic algorithm neural fusion battery pack, exploitativeness is strong.
Description of the drawings
To describe the technical solutions in the embodiments of the present invention more clearly, institute in being described below to the embodiment of the present invention
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the present invention
Example, for those of ordinary skill in the art, without creative efforts, can also implement according to the present invention
The content of example and these attached drawings obtain other attached drawings.
Fig. 1 is the method flow diagram of intelligent equalization method one embodiment of battery pack provided by the invention.
Fig. 2 is the method schematic of intelligent equalization method one embodiment of battery pack provided by the invention.
Fig. 3 is the block diagram of intelligent equalization system one embodiment of battery pack provided by the invention.
Specific embodiment
For make present invention solves the technical problem that, the technical solution that uses and the technique effect that reaches it is clearer, below
The technical solution of the embodiment of the present invention will be described in further detail with reference to attached drawing, it is clear that described embodiment is only
It is part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those skilled in the art exist
All other embodiments obtained under the premise of creative work are not made, belong to the scope of protection of the invention.
Embodiment 1
It please refers to Fig.1, is the method flow of intelligent equalization method one embodiment of battery pack provided by the invention
Figure.The intelligent equalization method of battery pack provided in an embodiment of the present invention can be applied to all kinds of lithium battery for electric vehicle groups and big
Medium-sized battery energy storage system etc..
The intelligent equalization method of the battery pack, including：
Step S101, neural network model is established, the input information of the neural network model includes the accumulative of battery pack
Cycle-index, accumulated discharge duration, the preceding once capacity of equilibrium, current discharge capacity, the remaining capacity of prediction, discharge-rate
At least one of in.
It should be noted that neutral net (Neural Networks, NN) (is claimed by substantial amounts of, simple processing unit
For neuron) complex networks system to be formed widely is interconnected, it is a highly complex non-linear dynamic study system
System.Neutral net has large-scale parallel, distributed storage and processing, self-organizing, adaptive and self-learning ability, is particularly suitable for locating
Reason needs information-processing problem that consider many factors and condition simultaneously, inaccurate and fuzzy.
Neural network model is described based on the mathematical model of neuron.Artificial neural network
(Artificial Nuearl Networks) is a kind of description to the first-order characteristics of human brain system.Simply, it
It is a mathematical model.Neural network model is represented by network topology, node feature and learning rules.
Wherein, " accumulated cycles " refer to the accumulated cycles of battery；" accumulated discharge duration " refers to the tired of battery
Meter electric discharge duration；Battery capacity (the root that " capacity of preceding once equilibrium " refers to last balancing procedure actual transfer or consume
According to specific balanced way, active balanced way carries out equilibrium by supplementing energy, and passive type balanced way passes through consumption
Energy carries out equilibrium)；" currently discharge capacity " refers to the capacity to have discharged among this discharge cycles process；" prediction
Remaining capacity " refer to remaining battery capacity (SOC) after this discharge cycles；" discharge-rate " refers to this discharge cycles
Discharge-rate.
Step S102, training sample group is established, for each battery pack in the training sample group, passes through genetic algorithm
Training neutral net, until the neural network model converge to output signal can meet default battery pack equalization performance will
It asks；Wherein, the output signal includes needing balanced capacity, opening the balanced time, open equilibrium for the battery pack
At least one of in electric current.
It should be noted that train neutral net by genetic algorithm, can realize the neutral net self without prison
Educational inspector practises, and neutral net is made to possess the stronger ability of equalization.
Genetic algorithm is the convergence algorithm of a kind of mimic biology circle natural evolution selection and genetic mechanism, by selecting at random
It selects, intersect and mutation operation, generate the individual that a group more adapts to environment, make Evolution of Population to more and more suitable region, finally
Obtain the optimal solution of problem.
Step S103, according to the output signal, the battery pack is carried out balanced.
Preferably, during equilibrium is carried out to the battery pack, neutral net can also continue to constantly self
It practises.
It, should in actual product in order to which this neutral net trained by genetic algorithm is allowed to possess the good ability of equalization
Following methods are used with middle：
1) for each battery in groups after in laboratory carry out the repeated charge of various operating modes and train neutral net；
2) the neutral net accuracy of equalization reaches requirement in laboratory；
It 3) will be in trained Application of Neural Network to product；
4) product further opens the continuous self-teaching of the neutral net in use, constantly optimizes, until
The end of life of product, it can be ensured that always constantly according to practical situations self-optimization after product export.
The intelligent equalization method of battery pack provided in an embodiment of the present invention can be based on genetic algorithm neural fusion electricity
Effective intelligent equalization of pond group, exploitativeness are strong.
Embodiment 2
It is second embodiment of the intelligent equalization method of battery pack provided by the invention below.The electricity of the embodiment of the present invention
The intelligent equalization method of pond group is on the basis of one embodiment, to training the concrete operations of neutral net by genetic algorithm
It is described in detail.
Should neutral net be trained by genetic algorithm, including：
Step S201, neural network model is established, the input information of the neural network model includes the accumulative of battery pack
Cycle-index, accumulated discharge duration, the preceding once capacity of equilibrium, current discharge capacity, the remaining capacity of prediction, discharge-rate
At least one of in.
Step S202, training sample group is established, for each battery pack in the training sample group, passes through genetic algorithm
Training neutral net, until the neural network model converge to output signal can meet default battery pack equalization performance will
It asks；Wherein, the output signal includes needing balanced capacity, opening the balanced time, open equilibrium for the battery pack
At least one of in electric current.
Wherein, the input information of the first time training of the neural network model is the capacity of default equilibrium, described pre-
If balanced capacity be voltage difference between every sub- battery according to battery pack and obtain.
Preferably for a set of battery pack, training neutral net, judges and opens equilibrium, it is necessary to root for the first time for the first time
According to practical experience by voltage difference between single battery etc. because usually being judged, the balanced electricity of the first time provided also needs
It to be provided by empirical algorithms.Wherein, the balanced electricity of first time is the capacity of default equilibrium.
It please refers to Fig.2, is the Method And Principle of intelligent equalization method one embodiment of battery pack provided by the invention
Figure.
The intelligent equalization method of battery pack provided by the invention, by establishing nerve network system come judgment of learning battery pack
Balanced opportunity.The neural network structure that the nerve network system uses is as shown in Fig. 2, be the neutral net mould of a standard
Type.
In actual application, the input information of neural network model selection is not limited to more than parameter, designs
Personnel can be according to actual demand and applicable cases increase and decrease input information.
In actual application, the neural network model selection output layer be also not limited to more than export signal,
Designer can increase and decrease input/output signal according to actual demand and applicable cases.
The number of plies of hidden layer and the quantity of every layer of hidden layer are simultaneously unlimited, it is necessary to go to determine according to practical application.
Preferably, it is described that neutral net is trained by genetic algorithm, including the following operation performed successively：
(1) initial weights, threshold value are generated for neutral net by indirect assignment and encoded；Increasing can also be passed through here
Add special weights " offset ", neutral net is made not consider further that weights, i.e., is normalized to threshold value in weights；Can also by with
Machine algorithm generates initial weights, threshold value for neutral net and encodes.
(2) using the weights of neutral net as digital chromosome, determine the nerve network input parameter, pass through nerve
Network carries out computing to input parameter, generates the network output of respective digital chromosome；
(3) fitness of chromosome is calculated according to fitness algorithm；This answers appropriate algorithm to be mainly used for judging nerve net
Actual battery portfolio effect caused by network output, and to this one fitness value of distribution, fitness value is more high, illustrates the dye
Neutral net performance under colour solid is better, and battery balanced effect is better.
(4) by selection algorithm, the higher chromosome of fitness is selected；Generally use roulette algorithm.
(5) by mutation algorithm and Hybrid Algorithm, selected chromosome is hybridized, make a variation generation a new generation kind
Group；This mutation algorithm and Hybrid Algorithm have no fixed algorithm, and the classic algorithm in common genetic algorithm can be used, also can basis
Lithium battery pack characteristic designed, designed algorithm.
(6) operation (2) is returned, can meet default battery pack until the neural network model converges to output signal
Equalization performance requirement, that is, obtain best initial weights and threshold value.
It should be noted that described need balanced capacity, including：
It is described to need balanced capacity to be transferred into or being shifted by balanced sub- battery needs for active balanced way
The energy gone out；
For passive type balanced way, the balanced capacity of the needs is the energy consumed by balanced sub- battery needs
Amount.
Step S203, according to the output signal, the battery pack is carried out balanced.
For the capacity for the needs equilibrium that neutral net has been calculated, according to the equilibrium side of the equal balance system of actual battery
Formula, the ability of equalization and discharge scenario determine to start and close balanced time and euqalizing current size (if can set
Determine euqalizing current).This point can be judged well by common algorithm.
Preferably, the battery pack is lithium battery group.
Lithium battery group is a kind of present widely used battery pack, and generalization is strong.
The intelligent equalization method of battery pack provided by the invention, establish neutral net battery balanced needs to shift to estimate
Electricity size trains the equilibrium calculation neutral net by genetic algorithm so that neutral net can be evolved with self, constantly excellent
Change is more preferably solved.
The intelligent equalization method of battery pack provided by the invention, using genetic algorithm neutral net to the equalization characteristic of battery
Carry out intelligence learning and hereditary variation optimization so that battery pack can depend on the good nonlinear fitting characteristic of neutral net, real
The method of existing intelligent equalization.
It is equal to carry out intelligent battery group based on genetic algorithm neutral net for the intelligent equalization method of battery pack provided by the invention
Weighing apparatus realizes the efficient balance of lithium battery in groups.
It is the embodiment of the intelligent equalization system of battery pack provided in an embodiment of the present invention below.The intelligent equalization of battery pack
The embodiment of system and the embodiment of the intelligent equalization method of above-mentioned battery pack belong to same design, the intelligent equalization of battery pack
The detail content of not detailed description in the embodiment of system, may be referred to the embodiment of the intelligent equalization method of above-mentioned battery pack.
The system realizes that the system is the functional software framework realized with computer program with computer program.
Embodiment 3
It please refers to Fig.3, is the structure box of intelligent equalization system one embodiment of battery pack provided by the invention
Figure.The intelligent equalization system of battery pack provided by the invention can be applied to all kinds of lithium battery for electric vehicle groups and large and medium-sized electricity
Pond energy-storage system etc..
The intelligent equalization system of the battery pack, including：
Unit is established, for establishing neural network model, the input information of the neural network model includes battery pack
Accumulated cycles, accumulated discharge duration, the preceding once capacity of equilibrium, current discharge capacity, the remaining capacity of prediction, electric discharge
At least one of in multiplying power；
Training unit for establishing training sample group, for each battery pack in the training sample group, passes through heredity
Algorithm for Training neutral net can meet the harmony of default battery pack until the neural network model converges to output signal
It can requirement；Wherein, the output signal includes needing balanced capacity, opening the balanced time, open for the battery pack
At least one of in the electric current of weighing apparatus；
Balanced unit, for according to the output signal, being carried out to the battery pack balanced.
The intelligent equalization system of battery pack provided in an embodiment of the present invention can be based on genetic algorithm neural fusion electricity
Effective intelligent equalization of pond group, exploitativeness are strong.
Embodiment 4
It is second embodiment of intelligent equalization system of battery pack provided by the invention below.It is provided in an embodiment of the present invention
The intelligent equalization system of battery pack is on the basis of one embodiment, to training the specific behaviour of neutral net by genetic algorithm
It is described in detail.
The intelligent equalization system of the battery pack, which is characterized in that including：
Unit is established, for establishing neural network model, the input information of the neural network model includes battery pack
Accumulated cycles, accumulated discharge duration, the preceding once capacity of equilibrium, current discharge capacity, the remaining capacity of prediction, electric discharge
At least one of in multiplying power；
Training unit for establishing training sample group, for each battery pack in the training sample group, passes through heredity
Algorithm for Training neutral net can meet the harmony of default battery pack until the neural network model converges to output signal
It can requirement；Wherein, the output signal includes needing balanced capacity, opening the balanced time, open for the battery pack
At least one of in the electric current of weighing apparatus；
Balanced unit, for according to the output signal, being carried out to the battery pack balanced.
Wherein, the input information of the first time training of the neural network model is the capacity of default equilibrium, described pre-
If balanced capacity be voltage difference between every sub- battery according to battery pack and obtain.
Wherein, it is described that neutral net is trained by genetic algorithm, including the following operation performed successively：
(1) initial weights, threshold value are generated for neutral net by indirect assignment and encoded；
(2) using the weights of neutral net as digital chromosome, determine the nerve network input parameter, pass through nerve
Network carries out computing to input parameter, generates the network output of respective digital chromosome；
(3) fitness of chromosome is calculated according to fitness algorithm；
(4) by selection algorithm, the higher chromosome of fitness is selected；
(5) by mutation algorithm and Hybrid Algorithm, selected chromosome is hybridized, make a variation generation a new generation kind
Group；
(6) operation (2) is returned, can meet default battery pack until the neural network model converges to output signal
Equalization performance requirement.
Wherein, it is described to need balanced capacity, including：
It is described to need balanced capacity to be transferred into or being shifted by balanced sub- battery needs for active balanced way
The energy gone out；
For passive type balanced way, the balanced capacity of the needs is the energy consumed by balanced sub- battery needs
Amount.
Wherein, the battery pack is lithium battery group.
The intelligent equalization system of battery pack provided by the invention, establish neutral net battery balanced needs to shift to estimate
Electricity size trains the equilibrium calculation neutral net by genetic algorithm so that neutral net can be evolved with self, constantly excellent
Change is more preferably solved.
The intelligent equalization system of battery pack provided by the invention, using genetic algorithm neutral net to the equalization characteristic of battery
Carry out intelligence learning and hereditary variation optimization so that battery pack can depend on the good nonlinear fitting characteristic of neutral net, real
The system of existing intelligent equalization.
It is equal to carry out intelligent battery group based on genetic algorithm neutral net for the intelligent equalization system of battery pack provided by the invention
Weighing apparatus realizes the efficient balance of lithium battery in groups.
A kind of intelligent equalization method of battery pack and intelligent equalization system can be based on genetic algorithm neural fusion electricity
Effective intelligent equalization of pond group, exploitativeness are strong.
One of ordinary skill in the art will appreciate that hardware can be passed through by realizing all or part of step of above-described embodiment
It completes, relevant hardware can also be instructed to complete by program, which can be stored in a computer-readable storage medium
In matter, storage medium can include memory, disk or CD etc..
More than content is only presently preferred embodiments of the present invention, according to the invention for those of ordinary skill in the art
Thought, there will be changes, this specification content should not be construed as to the present invention in specific embodiments and applications
Limitation.
Claims (6)
1. a kind of intelligent equalization method of battery pack, which is characterized in that including：
Neural network model is established, the input information of the neural network model includes the accumulated cycles of battery pack, adds up
At least one of in electric discharge duration, the preceding once capacity of equilibrium, current discharge capacity, the remaining capacity of prediction, discharge-rate；
Training sample group is established, for each battery pack in the training sample group, neutral net is trained by genetic algorithm,
It can meet the equalization performance requirement of default battery pack until the neural network model converges to output signal；Wherein, it is described
Exporting signal is included in the time for needing balanced capacity, unlatching balanced of the battery pack, the electric current for opening equilibrium at least
One；
According to the output signal, the battery pack is carried out balanced；
The input information of the first time training of the neural network model is the capacity of default equilibrium, the default equilibrium
Capacity is the voltage difference between every sub- battery according to battery pack and obtains；
It is described to need balanced capacity, including：
It is described to need balanced capacity to be transferred into or be transferred out of by balanced sub- battery needs for active balanced way
Energy；
For passive type balanced way, the balanced capacity of the needs is the energy consumed by balanced sub- battery needs.
2. the intelligent equalization method of battery pack according to claim 1, which is characterized in that described to be trained by genetic algorithm
Neutral net, including the following operation performed successively：
(1) initial weights, threshold value are generated for neutral net by indirect assignment and encoded；
(2) using the weights of neutral net as digital chromosome, determine the nerve network input parameter, pass through neutral net
Computing is carried out to input parameter, generates the network output of respective digital chromosome；
(3) fitness of chromosome is calculated according to fitness algorithm；
(4) by selection algorithm, the higher chromosome of fitness is selected；
(5) by mutation algorithm and Hybrid Algorithm, selected chromosome is hybridized, the generation that makes a variation population of new generation；
(6) operation (2) is returned, can meet the equilibrium of default battery pack until the neural network model converges to output signal
Performance requirement.
3. the intelligent equalization method of battery pack according to claim 1, which is characterized in that the battery pack is lithium battery
Group.
4. a kind of intelligent equalization system of battery pack, which is characterized in that including：
Unit is established, for establishing neural network model, the input information of the neural network model includes the accumulative of battery pack
Cycle-index, accumulated discharge duration, the preceding once capacity of equilibrium, current discharge capacity, the remaining capacity of prediction, discharge-rate
At least one of in；
Training unit for establishing training sample group, for each battery pack in the training sample group, passes through genetic algorithm
Training neutral net, until the neural network model converge to output signal can meet default battery pack equalization performance will
It asks；Wherein, the output signal includes needing balanced capacity, opening the balanced time, open equilibrium for the battery pack
At least one of in electric current；
Balanced unit, for according to the output signal, being carried out to the battery pack balanced；
The input information of the first time training of the neural network model is the capacity of default equilibrium, the default equilibrium
Capacity is the voltage difference between every sub- battery according to battery pack and obtains；
It is described to need balanced capacity, including：
It is described to need balanced capacity to be transferred into or be transferred out of by balanced sub- battery needs for active balanced way
Energy；
For passive type balanced way, the balanced capacity of the needs is the energy consumed by balanced sub- battery needs.
5. the intelligent equalization system of battery pack according to claim 4, which is characterized in that described to be trained by genetic algorithm
Neutral net, including the following operation performed successively：
(1) initial weights, threshold value are generated for neutral net by indirect assignment and encoded；
(2) using the weights of neutral net as digital chromosome, determine the nerve network input parameter, pass through neutral net
Computing is carried out to input parameter, generates the network output of respective digital chromosome；
(3) fitness of chromosome is calculated according to fitness algorithm；
(4) by selection algorithm, the higher chromosome of fitness is selected；
(5) by mutation algorithm and Hybrid Algorithm, selected chromosome is hybridized, the generation that makes a variation population of new generation；
(6) operation (2) is returned, can meet the equilibrium of default battery pack until the neural network model converges to output signal
Performance requirement.
6. the intelligent equalization system of battery pack according to claim 4, which is characterized in that the battery pack is lithium battery
Group.
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| CN106646260A (en) * | 2016-12-31 | 2017-05-10 | 深圳市沃特玛电池有限公司 | SOC estimation method for BMS system based on genetic neural network |
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| CN111509316B (en) * | 2020-04-29 | 2021-11-02 | 集美大学 | Marine lithium battery pack energy management method based on cycle life |
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